Emergency Manager Severe Weather Information Needs and Use of

Experimental Warning InformationDaphne LaDue, Ph.D.OU CAPS

Sean ErnstOU SoM

James HockerOklahoma Climatological Survey

Christopher Karstens, Ph.D.OU CIMMS/NSSL

James Correia, Jr., Ph.D.OU CIMMS/SPC

Jonathan WolfeNOAA NWS Charleston WV Forecast Office

This work was funded by NOAA/NSSL and OU CIMMS

MOTIVATION

Main Goal of FACETs:

• provide useful information to key decision makers

• to enable effective response

We can provide more, such as via Prototype Probabilistic Hazard Information

• Research Question: does the Prototype PHI tool enable forecasters to provide what emergency managers need?

Two parts to this work:

• Critical Incident Interviews

• 2015 Hazardous Weather Testbed

PART I:CRITICAL INCIDENT STUDY

Purpose: identify strengths and limitations of current severe weather information flow to EMs

Methodology: Critical Incident Technique (CIT) interviews

CIT interviews:

• go beyond participants’ opinions

• seek critical incidents that illustrate the competency (or lack thereof)

• developed in 1950s by a psychologist to help AF identify good pilots

Both Studies: data-driven, thematic analyses

PART I:PARTICIPANTS

Interviews:

• 5 county-level • 2 city-level• 1 state-level regional

coordinator (of 15 counties)

• 1 state level, ESF 8• 1 military• 1 EM for school district

PART I FINDINGS:STORM HISTORY

CIT stories revealed that storm history gives EMs a better idea of what to expect in their community:

Need to know what storm has done, expected strength changes — EM5

“In real time I was able to redirect [medical response resources], ‘cause I have the latest, greatest that the National Weather Service is providing…” —EM8

Storm history info and track help give 1-1.5 hours heads up on inbound storms and potential impact —EM4

“Getting to know that storm a little better” —EM9

PART I FINDINGS:RELATIONSHIP1. Specific events build relationships —EM7

• Forecaster on duty didn’t realize impact of sub-severe storm and EM needs related to impacts

• EM used existing relationships to solve problem• Built longer-term understanding of EM information needs

2. Building open lines of communication

• EMs know they can call NWS when they need to• NWS might even reach out prior to event

3. Knowing NWS forecasters creates trust in forecast information

Knowing NWS forecasters builds trust in information —EM5

“If I see things that concern me, I’ll either start chatting or get on the phone with my local NWS and ask them if they’re going to put a warning out” —EM2

PART I FINDINGS:CONFIDENCE1. EM’s understand forecasts carry uncertainty, and they’d like

to hear about forecaster opinions on it

2. Example of good information

Want confidence on threat timing and likelihood to shelter large events well in advance —EM4

“[NWSChat] gives me a certain level of confidence, and I find out what they really think too” —EM10

“It was high, I think it was high confidence, of, supercell development, into individual supercell development. Moderate to high confidence, that it will affect the metro, and then they gave the eta, like between 7 and 9 pm.” —EM11

PART I: PRELIMINARY OUTCOMES

1. EMs looking for the NWS story of a weather event

• Want the narrative of the storm as it unfolds• Want to know how the forecasters perceive the event

2. Want to build and maintain strong relationship with forecasters to build trust in forecast

3. Want forecaster’s insights into inherent uncertainty

• EMs aware that no forecast is exact, want to know forecaster’s honest assessment of forecast

PART II:HAZARDOUS WEA. TESTBED

Purpose: bring key stakeholder group into PHI development early in the R&D to assure resulting work is useful, usable

Methodology:

• Pre-week survey to establish current views of uncertainty

• EMs viewed PHI generated by NWS forecasters and noted decision/action points

• Researcher observations and questions

• Joint debriefing discussions after each case or live event

• End-of-week EM-only and joint w/NWS discussions

Traditional SVR warning polygon

PHI Object and SVR Plume

vs.

Polygon extends beyond echo behind and to the sides of the storm.Polygon forward spreads out in width.One polygon for tornado, wind, hail.

Object tightly surrounds intense part of echo.Plume forward spreads out in width.Separate objects for tornado vs. wind/hail.

HWT PARTICIPANTS

10 EMs from 5 states:Alabama (1)Michigan (1)Minnesota (1)Oklahoma (6)Wyoming (1)

6 NWS from 6 states:Arizona (1)Alabama (1)Maine (1)Missouri (1)Oklahoma (1)Virginia (1)

See also talk by Karstens et al. @ 9:45am Tuesday

PART II:HWT RESEARCH DESIGN

Forecasters in HWT working

w/PHI

EMs in another room, using EDD to see PHI output

Communication via NWSChat &

PHI

See also talk by Karstens et al. @ 9:45am Tuesday

Log of actions

Each object has an associated set of information (yellow box):

Contents of the discussion box evolved each week as forecasters and EMs interacted.

Time of departure

Time of arrival

FINDINGS:HAZARD PLUMES VS. TRADITIONAL WARNINGS

Advantages:

• PHI Focus

• Still need trigger points; EMs cannot devote 100% attention to weather

• Polygon Confidence of NWS of EM’s of others, too. “Confidence is contagious.”

The main difference: “Uncertainty” —Co4

Gives you what areas to focus on, and what areas likely won’t be affected —Co2

Helps identify which cells in a line might do something —St1

“...if you’re confident enough to...warn [x number of] people...maybe I should be certain, too.” —Co4

FINDINGS:PROBABILITY IS USEFUL.IS CHANGE MEANINGFUL?

Liked seeing the increases, decreases in probabilities

Changes after warning issuance could be meaningful

EMs: A few percentage change probably not meaningful, and may fluctuate too much.

• 10% was suggested,

• or have the forecaster tell decide what was is a meaningful increase or decrease for that day

“I’m at an 85%, where maybe the warning came out at a 60%, and that’s like, boom. It gives me a lot more information.” —Co5

FINDINGS:THIS IS MANAGEABLE

Initially some concern about the increase in information

Iterated w/ forecasters on discussion box contents

EMs started injecting information to the NWS.

Need spot forecast:Hail hitting hazardous chemical tanker truck, can’t take much more. Will the hail get any bigger? When will it stop?

Need spot forecast:College football team on a bus with bow echo heading toward the highway. How strong will winds be?

Need spot forecast:Water loading on factory roof, might collapse. How much longer will rain last?

“I think we need to send some realistic injects back to them so that the scenario works just a little bit better. They can...see some of the challenges that we have” —Co2

CO-CREATION OF WHAT PHI SHOULD BEAmazing dynamic — participants became researchers, asking each other insightful questions to understand the others’ point of view.

Each week iterated toward the same things:

• Discussion box to contain:

• history, such as reports• forecaster thinking, but not bland warning-type statements

• Forecaster touch critical; did not trust automated forecast

• Did forecaster agree? Or that they changed numbers with his/her expertise & knowledge beyond radar

NWS: “I’m not, I’m not completely, I’m not sold enough to drop the probabilities in light...of the reports we’ve gotten [and] how strong it was there for awhile.”

EM: ”Why didn’t you write that in the box?”

CONCLUSIONS

Critical Incident StudyEMs want the narrative of storm as it unfolds

Relationships need to be built and maintained

EMs want forecasters’ ongoing assessment, including uncertainty

Hazardous Weather Testbed PHI is more specific, focused useful for EM decisions

Still need trigger points for action; confidence of forecaster

On our Research in the HWT:

Presence of EMs gave forecasters focus & rapid feedback

Rapid co-creation of potential PHI vision

Contact:dzaras@ou.edu